A survey of algorithmic recourse: definitions, formulations, solutions, and prospects

8 October 2020

Gilles Barthe

Papers citing "A survey of algorithmic recourse: definitions, formulations, solutions, and prospects"

50 / 110 papers shown

Title
Generating Counterfactual and Contrastive Explanations using SHAP Shubham Rathi 40 56 0 21 Jun 2019
Explanations can be manipulated and geometry is to blame Ann-Kathrin Dombrowski Maximilian Alber Christopher J. Anders M. Ackermann K. Müller Pan Kessel AAML FAtt 49 329 0 19 Jun 2019
Issues with post-hoc counterfactual explanations: a discussion Thibault Laugel Marie-Jeanne Lesot Christophe Marsala Marcin Detyniecki CML 117 43 0 11 Jun 2019
Metric Learning for Individual Fairness Christina Ilvento FaML 49 97 0 01 Jun 2019
Model Agnostic Contrastive Explanations for Structured Data Amit Dhurandhar Tejaswini Pedapati Avinash Balakrishnan Pin-Yu Chen Karthikeyan Shanmugam Ruchi Puri FAtt 59 82 0 31 May 2019
Efficient candidate screening under multiple tests and implications for fairness Lee Cohen Zachary Chase Lipton Yishay Mansour 36 32 0 27 May 2019
Model-Agnostic Counterfactual Explanations for Consequential Decisions Amir-Hossein Karimi Gilles Barthe Borja Balle Isabel Valera 71 320 0 27 May 2019
Explainable Reinforcement Learning Through a Causal Lens Prashan Madumal Tim Miller L. Sonenberg F. Vetere CML 71 357 0 27 May 2019
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations R. Mothilal Amit Sharma Chenhao Tan CML 100 1,005 0 19 May 2019
Counterfactual Visual Explanations Yash Goyal Ziyan Wu Jan Ernst Dhruv Batra Devi Parikh Stefan Lee CML 56 510 0 16 Apr 2019
Equal Opportunity in Online Classification with Partial Feedback Yahav Bechavod Katrina Ligett Aaron Roth Bo Waggoner Zhiwei Steven Wu FaML 35 60 0 06 Feb 2019
An Evaluation of the Human-Interpretability of Explanation Isaac Lage Emily Chen Jeffrey He Menaka Narayanan Been Kim Sam Gershman Finale Doshi-Velez FAtt XAI 96 153 0 31 Jan 2019
Fairwashing: the risk of rationalization Ulrich Aïvodji Hiromi Arai O. Fortineau Sébastien Gambs Satoshi Hara Alain Tapp FaML 30 146 0 28 Jan 2019
Efficient Search for Diverse Coherent Explanations Chris Russell 51 236 0 02 Jan 2019
Interpretable Credit Application Predictions With Counterfactual Explanations Rory Mc Grath Luca Costabello Chan Le Van Paul Sweeney F. Kamiab Zhao Shen Freddy Lecue FAtt 35 109 0 13 Nov 2018
Contrastive Explanation: A Structural-Model Approach Tim Miller CML 39 167 0 07 Nov 2018
Explaining Explanations in AI Brent Mittelstadt Chris Russell Sandra Wachter XAI 81 664 0 04 Nov 2018
A Survey of Learning Causality with Data: Problems and Methods Ruocheng Guo Lu Cheng Jundong Li P. R. Hahn Huan Liu CML 49 169 0 25 Sep 2018
Actionable Recourse in Linear Classification Berk Ustun Alexander Spangher Yang Liu FaML 84 545 0 18 Sep 2018
Model Reconstruction from Model Explanations S. Milli Ludwig Schmidt Anca Dragan Moritz Hardt FAtt 39 177 0 13 Jul 2018
Handling Incomplete Heterogeneous Data using VAEs A. Nazábal Pablo Martínez Olmos Zoubin Ghahramani Isabel Valera 37 345 0 10 Jul 2018
On the Robustness of Interpretability Methods David Alvarez-Melis Tommi Jaakkola 50 524 0 21 Jun 2018
Towards Robust Interpretability with Self-Explaining Neural Networks David Alvarez-Melis Tommi Jaakkola MILM XAI 103 938 0 20 Jun 2018
Contrastive Explanations with Local Foil Trees J. V. D. Waa M. Robeer J. Diggelen Matthieu J. S. Brinkhuis Mark Antonius Neerincx FAtt 32 82 0 19 Jun 2018
Explaining Explanations: An Overview of Interpretability of Machine Learning Leilani H. Gilpin David Bau Ben Z. Yuan Ayesha Bajwa Michael A. Specter Lalana Kagal XAI 70 1,849 0 31 May 2018
Local Rule-Based Explanations of Black Box Decision Systems Riccardo Guidotti A. Monreale Salvatore Ruggieri D. Pedreschi Franco Turini F. Giannotti 110 436 0 28 May 2018
Black-box Adversarial Attacks with Limited Queries and Information Andrew Ilyas Logan Engstrom Anish Athalye Jessy Lin MLAU AAML 136 1,194 0 23 Apr 2018
Privacy-preserving Prediction Cynthia Dwork Vitaly Feldman 44 90 0 27 Mar 2018
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives Amit Dhurandhar Pin-Yu Chen Ronny Luss Chun-Chen Tu Pai-Shun Ting Karthikeyan Shanmugam Payel Das FAtt 89 587 0 21 Feb 2018
Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections Xin Zhang Armando Solar-Lezama Rishabh Singh FAtt 88 63 0 21 Feb 2018
Multimodal Explanations: Justifying Decisions and Pointing to the Evidence Dong Huk Park Lisa Anne Hendricks Zeynep Akata Anna Rohrbach Bernt Schiele Trevor Darrell Marcus Rohrbach 61 421 0 15 Feb 2018
Stealing Hyperparameters in Machine Learning Binghui Wang Neil Zhenqiang Gong AAML 121 461 0 14 Feb 2018
Prophit: Causal inverse classification for multiple continuously valued treatment policies Michael T. Lash Qihang Lin W. Street CML 25 3 0 14 Feb 2018
A Survey Of Methods For Explaining Black Box Models Riccardo Guidotti A. Monreale Salvatore Ruggieri Franco Turini D. Pedreschi F. Giannotti XAI 81 3,922 0 06 Feb 2018
Inverse Classification for Comparison-based Interpretability in Machine Learning Thibault Laugel Marie-Jeanne Lesot Christophe Marsala X. Renard Marcin Detyniecki 108 100 0 22 Dec 2017
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR Sandra Wachter Brent Mittelstadt Chris Russell MLAU 67 2,332 0 01 Nov 2017
Latent Space Oddity: on the Curvature of Deep Generative Models Georgios Arvanitidis Lars Kai Hansen Søren Hauberg DRL 78 267 0 31 Oct 2017
Interpretation of Neural Networks is Fragile Amirata Ghorbani Abubakar Abid James Zou FAtt AAML 104 861 0 29 Oct 2017
Interpretability via Model Extraction Osbert Bastani Carolyn Kim Hamsa Bastani FAtt 47 129 0 29 Jun 2017
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 265 2,248 0 24 Jun 2017
Explanation in Artificial Intelligence: Insights from the Social Sciences Tim Miller XAI 217 4,229 0 22 Jun 2017
Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking Gabriele Tolomei Fabrizio Silvestri Andrew Haines M. Lalmas 29 207 0 20 Jun 2017
Fair Inference On Outcomes Razieh Nabi I. Shpitser FaML 38 349 0 29 May 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 343 3,742 0 28 Feb 2017
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 290 1,849 0 03 Feb 2017
Universal adversarial perturbations Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi Omar Fawzi P. Frossard AAML 108 2,520 0 26 Oct 2016
Generalized Inverse Classification Michael T. Lash Qihang Lin W. Street Jennifer G. Robinson Jeffrey W. Ohlmann 37 60 0 05 Oct 2016
Stealing Machine Learning Models via Prediction APIs Florian Tramèr Fan Zhang Ari Juels Michael K. Reiter Thomas Ristenpart SILM MLAU 68 1,798 0 09 Sep 2016
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 160 8,497 0 16 Aug 2016
The Mythos of Model Interpretability Zachary Chase Lipton FaML 110 3,672 0 10 Jun 2016